Hi Cam,
Thank you for your response. I am glad to see you participating on
this list, besides SEMNET and MULTILEVEL. Below, I pasted output from
the -xtmixed- run (preferable with mixed linear models), which I
mentioned suggests groups matter. The variable police force is scaled
along a severity metric (minimum force 1 to maximum force 9), ratings
obtained from a panel of officers. Varieties of force weighted with
average ratings. I agree with the no value-added in practice, but have
concerns about theoretical violations (maybe with reviewers). Your
thoughts? Thank you.
Best,
Frank
xtmixed pforce || pd:, mle variance
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -3790.7576
Iteration 1: log likelihood = -3790.7576
Computing standard errors:
Mixed-effects ML regression Number of obs
= 3300
Group variable: pd Number of groups
= 16
Obs per group: min
= 22
avg
= 206.2
max
= 696
Wald chi2(0)
= .
Log likelihood = -3790.7576 Prob > chi2
= .
------------------------------------------------------------------------------
pforce | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------
+----------------------------------------------------------------
_cons | 3.365989 .0380829 88.39 0.000
3.291348 3.44063
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf.
Interval]
-----------------------------
+------------------------------------------------
pd: Identity |
var(_cons) | .0177083 .0073768 .
0078269 .040065
-----------------------------
+------------------------------------------------
var(Residual) | .5776353 .0142484 .
5503734 .6062477
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 102.86 Prob >= chibar2
= 0.0000
On Aug 10, 2009, at 10:18 PM, Cameron McIntosh wrote:
Hi Frank,
You use an ordered probit model, yet your DV appears to have 23
categories (22 thresholds).
Could this be the problem? Perhaps simply modeling it as continuous
would be more appropriate. With 23 categories, I don't think modeling
the latent response variate y* offers much value-added over modeling
the observed y.
Cam
----------------------------------------
From: [email protected]
To: [email protected]
Subject: st: estimation using gllamm, oprobit model fails to converge
Date: Mon, 10 Aug 2009 18:57:28 -0400
Hi All,
I used -gllamm- to run a Random Intercepts-Only Model. Below is the
output. The DV is ordinal, and believed to have a continuous latent
continuum. I am teaching myself multilevel modeling, Stata, and the
_gllamm- command. I am using Stata Version 11. Would the below failure
to converge suggest that there is little variability between j groups
on the DV? "or" Did I do something wrong in the model specification? I
found that this model - xtmixed pforce || pd:, mle variance -
converged and yielded a significant between-group difference that
suggested groups mattered. I would greatly appreciate any guidance and
resources. I have been using Rabe-Hesketh & Skrondal's (2008) book for
Stata. Thank you.
Best,
Frank
. gllamm pforce, i(pd) nip(12) link(oprobit) adapt trace
General model information
------------------------------------------------------------------------------
dependent variable: pforce
ordinal responses: oprobit
equations for fixed effects
_cut11: _cons
_cut12: _cons
_cut13: _cons
_cut14: _cons
_cut15: _cons
_cut16: _cons
_cut17: _cons
_cut18: _cons
_cut19: _cons
_cut110: _cons
_cut111: _cons
_cut112: _cons
_cut113: _cons
_cut114: _cons
_cut115: _cons
_cut116: _cons
_cut117: _cons
_cut118: _cons
_cut119: _cons
_cut120: _cons
_cut121: _cons
_cut122: _cons
Random effects information for 2 level model
------------------------------------------------------------------------------
***level 2 (pd) equation(s):
standard deviation of random effect
pd1: _cons
number of level 1 units = 3300
number of level 2 units = 16
Initial values for fixed effects
Iteration 0: log likelihood = -2735.2811
Ordered probit estimates Number of obs
= 3300
LR chi2(0)
= 0.00
Prob> chi2
= .
Log likelihood = -2735.2811 Pseudo R2
= 0.0000
------------------------------------------------------------------------------
pforce | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------
+----------------------------------------------------------------
-------------
+----------------------------------------------------------------
_cut1 | -1.583387 .035338 (Ancillary parameters)
_cut2 | 1.196465 .0285592
_cut3 | 1.19802 .0285798
_cut4 | 1.202704 .0286423
_cut5 | 1.269557 .0295796
_cut6 | 1.271259 .0296046
_cut7 | 1.276389 .0296804
_cut8 | 1.464599 .0328633
_cut9 | 1.466823 .032906
_cut10 | 1.524945 .0340702
_cut11 | 1.529823 .0341722
_cut12 | 1.674974 .0375413
_cut13 | 1.678071 .0376208
_cut14 | 1.684313 .037782
_cut15 | 1.806059 .0412223
_cut16 | 1.809953 .0413422
_cut17 | 1.947163 .0460173
_cut18 | 2.262989 .0610329
_cut19 | 2.349713 .0665442
_cut20 | 2.361894 .0673782
_cut21 | 3.236012 .2017793
_cut22 | 3.428888 .2713744
------------------------------------------------------------------------------
------------------------------------------------------------------------------
start running on 10 Aug 2009 at 17:55:00
Running adaptive quadrature
------------------------------------------------------------------------------
Iteration 0 of adaptive quadrature:
Initial parameters:
_cut11: _cut12: _cut13: _cut14: _cut15:
_cut16: _cut17: _cut18: _cut19: _cut110: _cut111:
_cons _cons _cons _cons _cons
_cons _cons _cons _cons _cons _cons
y1 -1.583387 1.196465 1.19802 1.202704 1.269557 1.271259
1.276389 1.464599 1.466823 1.524945 1.529823
_cut112: _cut113: _cut114: _cut115: _cut116:
_cut117: _cut118: _cut119: _cut120: _cut121: _cut122:
_cons _cons _cons _cons _cons
_cons _cons _cons _cons _cons _cons
y1 1.674974 1.678071 1.684313 1.806059 1.809953 1.947163
2.262989 2.349713 2.361894 3.236012 3.428888
pd1:
_cons
y1 .5
Updated log likelihood:
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 Convergence
not achieved: try with more quadrature points
finish running on 10 Aug 2009 at 17:55:31
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